This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.

Abstract

The growing popularity of Free and Open Source (FOSS) GIS software is without doubts due to the possibility to build and customize geospatial applications to meet specific requirements for any users. From this point of view, QGIS is one of the most flexible as well as fashionable GIS software environment which enables users to develop powerful geospatial applications using Python. Exploiting this feature, we present here a first prototype plugin for QGIS dedicated to Hotspot analysis, one of the techniques included in the Exploratory Spatial Data Analysis (ESDA). These statistics aim to perform analysis of geospatial data when spatial autocorrelation is not neglectable and they are available inside different Python libraries, but still not integrated within the QGIS core functionalities. The main plugin features, including installation requirements and computational procedures, are described together with an example of the possible applications of the Hotspot analysis.

Author Comment

This version of the paper was formatted according the the guidelines provided by OGRS committee.

Figure_2

Figure_3

Figure_4

Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Daniele Oxoli conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work.

Mayra A Zurbarán conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, performed the computation work, reviewed drafts of the paper.

Stanly Shaji performed the experiments, contributed reagents/materials/analysis tools, performed the computation work.

Arun K Muthusamy performed the experiments, contributed reagents/materials/analysis tools, performed the computation work.

Data Deposition

The following information was supplied regarding data availability:

GitHub (https://github.com/stanly3690/HotSpotAnalysis_Plugin)

Funding

Feedback on other revisions

Feedback on this revision

1

This work presents the integration of the PySAL Gi hotspot detection in QGIS. This plugin is a first step towards the insertion of PySAL in QGIS which would improve drastically the QGIS Exploratory Spatial Data Analysis functionalities. The hotspot detection is illustrated with the analysis of shared GPS sport tracks during weekdays and weekends. A heatmap is compared with the result of the Gi hot spots analysis. Some remarks which may improve the quality of the paper: (1) Heatmaps: Usually, the heatmaps are computed with a Kernel Density Estimation (which is statistically well defined). Does the heatmap plugin implement a KDE or is it a blackbox function? (2) In p.5 "The main drawback of using heatmaps lies in the fact that both, the type of density function and the visualization parameters -adopted to produce the output map- strongly affect the result." does not agree with the KDE literature which says that the type of density functions has less impact than the choice of the proper threshold. (VA Epanechnikov. Non-parametric estimation of a multivariate probability density. Theory of Probability and its Applications, 1969) (3) Waypoints: Do the GPX tracks have the same sampling rate (for instance one point per minute). If no, did you resample the tracks to have a more homogeneous dataset? (4) Spatial unit: Why did you choose the municipalities rather than an other spatial unit (for instance a regular grid)?

Add your feedback

Before adding feedback, consider if it can be asked as a question instead, and if so then use the Question tab. Pointing out typos is fine, but authors are encouraged to accept only substantially helpful feedback.

Follow this preprint for updates

"Following" is like subscribing to any updates related to a preprint.
These updates will appear in your home dashboard each time you visit PeerJ.

You can also choose to receive updates via daily or weekly email digests.
If you are following multiple preprints then we will send you
no more than one email per day or week based on your preferences.

Note: You are now also subscribed to the subject areas of this preprint
and will receive updates in the daily or weekly email digests if turned on.
You can add specific subject areas through your profile settings.